Electromagnetic　(EM)　parameterized　modeling　is　important　for　EM　repetitive　analysis,　such　as　EM　optimization,　what　if　analysis,　and　yield　optimization.　An　overview　of　advances　in　artificial　neural　networks　(ANNs)　for　EM　parameterized　modeling　is　presented　in　this　paper,　covering　forward/inverse　modeling,　deep　neural　networks,　knowledge-based　neural　networks,　neuro-transfer　functions,　and　applications　for　fast　EM　modeling　with　varying　values　in　geometrical　parameters.